Visual Analytics of Multilayer Networks Across Disciplines (Dagstuhl Seminar 19061)

This report documents the program and the outcomes of Dagstuhl Seminar 19061 "Visual Analytics of Multilayer Networks Across Disciplines". Networks, used to understand systems, often contain multiple types of nodes and/or edges. They are often flattened to a single network, even though real-world systems are more accurately modelled as a set of interacting networks, or layers, with different node and edge types. These are so-called multilayer networks. These networks are studied by researchers both in network visualization and in complex systems -- the domain from which the concept of multilayer networks has recently emerged. Moreover, researchers in various application domains study these systems, e.g. biology, digital humanities, sociology and journalism. These research areas have shown parallel individual developments. Therefore, one of the aims of the seminar was to bring together an interdisciplinary community of researchers and practitioners of different disciplines. This interdisciplinary community discussed existing solutions, open challenges and future research directions for visual analytics of multilayer networks across disciplines. The seminar was attended by researchers from information visualization, visual analytics, complex systems and application domains. The application domains covered digital humanities, social sciences, biological sciences, and in public health research (25% of attendees were from these fields). The seminar not only provided multiple application domains for the visualization experts, but also also provided the domains experts with different groups of visualization experts in breakouts sessions, to expose them to multiple approaches to solving their problems. Building on this close working relationship between the visualization and domain experts, working groups were defined to determine which are the important challenges for multilayer network visualization. A number of sub-topics were identified that require further research: A unifying visualization framework, Novel Visual Encodings, Analytic and Attributes, Interaction, Evaluation, Use Cases and Human Factors. The outcomes of the seminar should stimulate collaborative research on these topics between our community, complex networks, and wide range of application domains for the visual analytics of multilayer networks